想请教关于人脸识别 LBP子模式的代码,不知道在openmv上该怎么实现,因为运行例程里的LBP算法,感觉效果并不好,看论文有看到LBP子模式啊,金字塔方式的多尺度LBP啊之类的,想请教下各位大神该怎么做。
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想请教关于人脸识别 LBP子模式的代码
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关于人脸检测矩形框不准或不显示的问题
import sensor,image,time sensor.reset() # Initialize the camera sensor. sensor.set_contrast(1) sensor.set_gainceiling(16) sensor.set_pixformat(sensor.GRAYSCALE) sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others) sensor.skip_frames(time = 2000) # Let new settings take affect. # Load up a face detection HaarCascade. This is object that your OpenMV Cam # can use to detect faces using the find_features() method below. Your OpenMV # Cam has fontalface HaarCascade built-in. By default, all the stages of the # HaarCascade are loaded. However, You can adjust the number of stages to speed # up processing at the expense of accuracy. The frontalface HaarCascade has 25 # stages. face_cascade = image.HaarCascade("frontalface", stages=25) print(face_cascade) while(True): img = sensor.snapshot() # Threshold can be between 0.0 and 1.0. A higher threshold results in a # higher detection rate with more false positives. The scale value # controls the matching scale allowing you to detect smaller faces. faces = img.find_features(face_cascade, threshold=0.75, scale_factor=1.35) for r in faces: img.draw_rectangle(r)
尝试过更改stage,threshold和scale_factor,但无法像视频教程14那样持续且准确地框出人脸,stage偏大时无法显示矩形框,想请问是openmv自带模型的问题?摄像头的问题?计算机配置问题?还是其他问题?
print(face_cascade)在串行终端显示为"width":24, "height":24, "n_stages":25, "n_features":2913, "n_rectangles":6383 -
RE: snapshot时osError:failed to write requested bytes
@kidswong999 谢谢指导!前面试了一下,把电脑关机后我先插了SD卡,然后开电脑,最后连硬件,但问题依然存在,请问我的顺序正确吗?
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RE: snapshot时osError:failed to write requested bytes
import sensor, image, pyb RED_LED_PIN = 1 BLUE_LED_PIN = 3 sensor.reset() # Initialize the camera sensor. sensor.set_pixformat(sensor.GRAYSCALE) sensor.set_framesize(sensor.HQVGA) # or sensor.QQVGA (or others) sensor.skip_frames(time = 2000) # Let new settings take affect. # Load up a face detection HaarCascade. This is object that your OpenMV Cam # can use to detect faces using the find_features() method below. Your OpenMV # Cam has fontalface HaarCascade built-in. By default, all the stages of the # HaarCascade are loaded. However, You can adjust the number of stages to speed # up processing at the expense of accuracy. The frontalface HaarCascade has 25 # stages. face_cascade = image.HaarCascade("frontalface", stages=25) while(True): pyb.LED(RED_LED_PIN).on() print("About to start detecting faces...") sensor.skip_frames(time = 2000) # Give the user time to get ready. pyb.LED(RED_LED_PIN).off() print("Now detecting faces!") pyb.LED(BLUE_LED_PIN).on() diff = 10 # We'll say we detected a face after 10 frames. while(diff): img = sensor.snapshot() # Threshold can be between 0.0 and 1.0. A higher threshold results in a # higher detection rate with more false positives. The scale value # controls the matching scale allowing you to detect smaller faces. faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5) if faces: diff -= 1 for r in faces: img.draw_rectangle(r) pyb.LED(BLUE_LED_PIN).off() print("Face detected! Saving image...") sensor.snapshot().save("snapshot-%d.jpg" % pyb.rng()) # Save Pic. 前面运行正常,能框出人脸,最后一步报错,报错内容见标题